The invention discloses a hyperspectral image super-resolution optimization method based on a fused three-
branch network, and belongs to the field of
hyperspectral image processing, and the method comprises the steps: firstly obtaining and preprocessing training data, collecting LR-HSI, HR-MSI and corresponding high-resolution hyperspectral truth values, and completing normalization, data enhancement and cross-
modal feature dimension matching and interaction; constructing a three-
branch network model, inputting the LR-HSI into an HSI
branch to extract spectral features, and inputting the HR-MSI into an MSI branch to extract spatial features; performing residual calculation and
noise suppression on the spectral and spatial features, inputting the spectral and spatial features into a fusion branch, generating compensation features through
convolution and jump connection, fusing three-branch output through learnable weight, generating a
fusion image, and training a model through an optimizer. According to the method, a residual difference decoupling fusion strategy is introduced, and directional repair and enhancement are respectively carried out on missing space information in a hyperspectral image and insufficient spectral information in a
multispectral image.